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Detecting events on time series data generated by sensors has received a great amount of attention with increasingly deployment of variable sensors. In this paper, we propose a novel framework for classifying events upon sensors data called BEC. Given long raw time series and event labels on fuzzy time points, BEC extracts burst-based features to represent the events. There are mainly two important...
Due to the data diversity and complexity in industrial system, the accuracy of data-based modeling might be largely affected by such a series of issues. Aiming at the energy system in steel industry, this study proposes a fuzzy modeling based on Gaussian membership expression. First, in the stage of sample selection, the industrial data set is divided into a number of clusters, from which the representative...
Echo state networks (ESNs), that exhibit good performance for modeling a nonlinear or non-Gaussian dynamic system, have been widely used for time series prediction. However, estimating the output weights of the ESNs remains intractable. Extended Kalman filter (EKF) is an effective estimate method, but its computational cost is relatively high. In this study, a Map Reduce framework based parallel zed...
Prediction intervals that provide estimated values as well as the corresponding reliability are applied to nonlinear time series forecast. However, constructing reliable prediction intervals for noisy time series is still a challenge. In this paper, a bootstrapping reservoir computing network ensemble (BRCNE) is proposed and a simultaneous training method based on Bayesian linear regression is developed...
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